library(Seurat)
## Loading required package: ggplot2
## Loading required package: cowplot
## 
## Attaching package: 'cowplot'
## The following object is masked from 'package:ggplot2':
## 
##     ggsave
## Loading required package: Matrix
seurobj <- readRDS('/projects/pytrik/sc_adipose/analyze_10x_fluidigm/data/10x-180831')
aligned <- readRDS('/projects/pytrik/sc_adipose/analyze_10x_fluidigm/data/10x-180831-aligned')
aligned_T1_old <- readRDS('/projects/pytrik/sc_adipose/analyze_10x_fluidigm/data/10x-180504-T1-aligned')

Quality control plots

For now the data was only filtered on 0.1 percent.mito.

VlnPlot(seurobj, c("nGene", "percent.mito", "nUMI"), group.by='timepoint', nCol = 1, point.size.use=-1, size.x.use = 10)

GenePlot(seurobj, 'nUMI', 'nGene', cex.use = 0.5)

PCElbowPlot(seurobj) #15 pcs?

Data visualization

PCAPlot(seurobj, group.by='timepoint', pt.size=0.1)

TSNEPlot(seurobj, group.by='timepoint', pt.size=0.1)

TSNEPlot(seurobj, group.by='Phase', pt.size=0.1)

TSNEPlot(seurobj, group.by='res.0.5', pt.size=0.1)

FeaturePlot(seurobj, features.plot = 'nUMI', cols.use=c('grey', 'blue'), no.legend=F)

FeaturePlot(seurobj, features.plot = 'percent.mito', cols.use=c('grey', 'blue'), no.legend = F)

FeaturePlot(seurobj, features.plot = 'nGene', cols.use=c('grey', 'blue'), no.legend = F)

Alignment

Shared correlation per CC.

load('/projects/pytrik/sc_adipose/analyze_10x_fluidigm/data/MetageneBicorPlots')
ccplot_aligned
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

12 CC’s were used for tSNE.

TSNEPlot(aligned, group.by='timepoint', pt.size=0.1)

TSNEPlot(aligned, group.by='Phase', pt.size=0.1)

Aligning T1 with old data

Shared correlation per CC.

ccplot_aligned_T1_old
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

15 CC’s were used for tSNE.

#replacing res.0.5 column of T1 with 'T1'
aligned_T1_old@meta.data[aligned_T1_old@meta.data$sample_name == 'T1', 'res.0.5'] = 'T1'

TSNEPlot(aligned_T1_old, group.by='sample_name', pt.size=0.1)

TSNEPlot(aligned_T1_old, group.by='res.0.5', pt.size=0.1)

Highligted mixture cluster

DimPlot(aligned_T1_old, reduction.use='tsne', cells.highlight=aligned_T1_old@cell.names[which(aligned_T1_old@meta.data$res.0.5 %in% 12)], pt.size.use=0.1, cols.highlight = 'blue', cols.use='grey')

Highlighted T1 cells. It does not look like T1 cells are in the mixture cluster on the right. Could it be the big cluster in T1 with low UMI and gene counts affects the sample alignment and that’s the reason we are not seeing cells of T1 in the mixture cluster? Or because T1 consists of four samples? There are also some cells from the mixture cluster scattered accross the whole dataset, so maybe the mixture cells from T1 are there but also scattered accross the data.

DimPlot(aligned_T1_old, reduction.use='tsne', cells.highlight=aligned_T1_old@cell.names[which(aligned_T1_old@meta.data$res.0.5 %in% 'T1')], pt.size.use=0.1, cols.use = 'grey', cols.highlight = 'blue')

We can also look at some of the marker genes for the mixture cluster.

T1 <- readRDS('/projects/pytrik/sc_adipose/analyze_10x_fluidigm/data/10x-180831-T1')

Positive markers. (the clustering was performed on the whole dataset)

VlnPlot(T1, group.by='res.0.5', nCol=2, features.plot=c('ACTB', 'TPM2', 'ANXA2', 'LDHA', 'PRDX1', 'MYL12B', 'MYL12A', 'CCND1', 'RAB13', 'MAP1B'), point.size.use=-1)

Negative markers

VlnPlot(T1, group.by='res.0.5', nCol=2, features.plot=c('MALAT1', 'COL1A1', 'NEAT1', 'FN1', 'COL1A2', 'HIST1H4C', 'CYR61', 'COL3A1'), point.size.use=-1)

It looks like cluster 11 marks the mixture cluster. The clustering here was performed on the whole dataset. Let’s see where cluster 11 is:

TSNEPlot(seurobj, group.by='res.0.5', pt.size=0.1)

Does cluster 11 also contain cells from other timepoints?

table(seurobj@meta.data$timepoint[seurobj@meta.data$res.0.5 == 11])
## 
## T1 T2 T3 T4 T5 T6 
## 12 27 26  3  3  0

Markergene expression

FeaturePlot(seurobj, features.plot = 'EBF2', cols.use=c('grey', 'blue'), no.legend = F)

FeaturePlot(seurobj, features.plot = 'TM4SF1', cols.use=c('grey', 'blue'), no.legend = F)

FeaturePlot(seurobj, features.plot = 'LY6K', cols.use=c('grey', 'blue'), no.legend = F)

FeaturePlot(seurobj, features.plot = 'PDGFRA', cols.use=c('grey', 'blue'), no.legend = F)